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Mechanized and intelligent flower thinning is a high-speed flower thinning method nowadays. The classification and detection of flowers and flower buds are the basic requirements to ensure the normal operation of the flower thinning machine. Aiming at the problems of pear inflorescence detection and classification in the current intelligent production of pear orchards, a Y-shaped shed pear orchard inflorescence recognition algorithm Ghost-YOLOv5s-BiFPN based on improved YOLOv5s was proposed in this research. The detection model was obtained by labeling and expanding the pear tree bud and flower images collected in the field and sending them to the algorithm for training. The Ghost-YOLOv5s-BiFPN algorithm used the weighted bidirectional feature pyramid network to replace the original path aggregation network structure, and effectively fuse the features of different sizes. At the same time, ghost module was used to replace the traditional convolution, so as to reduce the amount of model parameters and improve the operation efficiency of the equipment without reducing the accuracy. The field experiment results showed that the detection accuracy of the Ghost-YOLOv5s-BiFPN algorithm for the bud and flower in the pear inflorescence were 93.21% and 89.43%, respectively, with an average accuracy of 91.32%, and the detection time of a single image was 29 ms. Compared with the original YOLOv5s algorithm, the detection accuracy was improved by 4.18%, and the detection time and model parameters were reduced by 9 ms and 46.63% respectively. Compared with the original YOLOV5s network, the mAP and recall rate were improved by 4.2% and 2.7%, respectively; the number of parameters, model size and floating point operations were reduced by 46.6%, 44.4% and 47.5% respectively, and the average detection time was shortened by 9 ms. With Ghost convolution and BIFPN adding model, the detection accuracy has been improved to a certain extent, and the model has been greatly lightweight, effectively improving the detect efficiency. From the thermodynamic diagram results, it can be seen that BIFPN structure effectively enhances the representation ability of features, making the model more effective in focusing on the corresponding features of the target. The results showed that the algorithm can meet the requirements of accurate identification and classification of pear buds and flowers, and provide technical support for the follow-up pear garden to achieve intelligent flower thinning. [ABSTRACT FROM AUTHOR]

Thinning is one of the three labor-consuming sections (pruning, thinning and harvesting) in orchard management process. Chemical thinning, not only unstable but also reduces fruit safety, has been banned gradually in recent years. Pure artificial thinning, especially artificial fruit thinning, needs larger workload and higher labor cost, unsuitable seriously for the requirements of modern orchard planting. Two kinds of pear mechanical flower thinners were designed in the paper. They were three arms tractor-mounted flower thinner (TMFT) and hand-held flower thinner (HHFT), to alleviate the heavy labor pressure in the process of thinning and improve the efficiency of orchard operation. The width of TMFT was controlled by hydraulic cylinder that range from 1.2 to 1.7 m. TMFT rotation speed of thinning shaft that range from 0 to 300 r/min was controlled by electronic governor. The length of HHFT can change through adjusting the extension rod that range from 0.95 to 1.6 m. HHFT rotation speed of thinning shaft that range from 0 to 900 r/min was controlled by electronic governor too. ‘Y’ trellis ‘Cuiguan’ pear was taken as the research object in the paper. TMFT impact forces of thinning rope with different rotation speed of thinning shaft and thinning distance were measured in bench test, to find the working range of rotation speed. Field test setting 50% flower bud preserved rate (it is easier to thin flower buds than flowers) and 0.44 m/s tractor work speed, and the best rotation speed of thinning shaft was ensured: 254 r/min. Flower bud reserve rate, fruit set rate, work efficiency, fruit yield and quality of four kinds of operation methods were tested, which were TMFT combined with artificial fruit thinning, HHFT combined with artificial fruit thinning, artificial flower fruit thinning and artificial fruit thinning only. Twenty-four ‘Y’ trellis pear trees (6 a) with almost same size were selected for four operation methods. The canopies of each tree were divided into the upper middle lower three layers and five branches were selected as samples in each layer. In order to reduce the operation error, except the TMFT, the operation of each group was completed by two workers with experienced job skill. Field test show that three flower thinning methods can meet the requirement of 50% flower bud preserved rate, the coefficient of variation all less than 15%. There is no obvious difference between TMFT and artificial method in thinning quality and the stability of TMFT is the highest among test groups. Mechanical and artificial flower thinning can significantly reduce about 50% absolute fruit set rate. TMFT will damage the young fruits in a certain degree, but does not affect the yield and quality in the last. Work efficiency of TMFT was highest among flower thinning methods and 66.17% artificial fruit thinning time can be saved. TMFT has a higher requirement for pear orchard mechanization degree, suitable for large-scale mechanized pear orchard. Work efficiency of HHFT is second only to TMFT, 37.26% artificial fruit thinning time can be saved. HHFT is suitable for small and medium sized pear orchard. For fruit yield and quality, total and high quality fruit yield per tree of pure artificial fruit thinning group is higher 11.37% and 8.59% respectively than average values of three kinds of flower thinning group, but the average weight of high quality fruit and brix of sugar solids of is lower 6.58% and 1.55% respectively. The research is helpful to improve the working quality of flower thinner and provide reference for the mechanization management of pear orchard. [ABSTRACT FROM AUTHOR]

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